clusterAlgorithmHclust-class {goCluster} | R Documentation

## Class "clusterAlgorithmHclust", provides hierarchical clustering for goCluster.

### Description

This can be used to group an expression dataset according to hierarchical clustering. The resulting gene groups can subsequently be analysed for significant enrichment of specific annotations.

### Details

The class provides a wrapper around the `clusterhclust` function. Please read the corresponding documentation for further details.

### Slots

`tree`:
  Object of class `"hclust"`, holds another copy of the clustering result (the direct result from the call to hclust).
`method`:
  Object of class `"character"`, defines the agglomeration method that is going to be used for the hierarchical clustering.
`distance`:
  Object of class `"character"`, specifies the distance matrix that will be used.

Additional slots are described in the documentation of the `clusterAlgorithm-class` and `clusterModule-class`.

### Extends

Class `"clusterAlgorithm"`, directly. Class `"clusterModule"`, by class `"clusterAlgorithm"`.

### Methods

config
  `signature(object = "clusterAlgorithmHclust")`: interactive setup of the class. You will be asked to specify the distance matrix as well as the agglomeration method.
setup
  `signature(object = "clusterAlgorithmHclust")`: returns the configuration of the object as a list. This list can again be used for the non-interactive setup of the class.
setup<-
  `signature(object = "clusterAlgorithmHclust")`: non-interactive setup of the class. The options are specified using a list.
execute
  `signature(object = "clusterAlgorithmHclust")`: run the clustering.
reset
  `signature(object = "clusterAlgorithmHclust")`: remove all cluster data so that the `execute` function can be run again.
print
  `signature(object = "clusterAlgorithmHclust")`: This function prints some basic information about the content of this object.

### Author(s)

Gunnar Wrobel, http://www.gunnarwrobel.de.

### See Also

`clusterhclust`, `goCluster-class`, `clusterModule-class`, `clusterAlgorithm-class`, `clusterAlgorithmKmeans-class`, `clusterAlgorithmClara-class`, `clusterAlgorithmPam-class`

### Examples

    ## Predefined setup for goCluster
    data(benomylsetup)

    ## Change the setup to
    ## hierarchical clustering
    benomylsetup$data$dataset  <- benomylsetup$data$dataset[1:200,]
    benomylsetup$data$uniqueid <- benomylsetup$data$uniqueid[1:200]
    benomylsetup$classalgo <- "clusterAlgorithmHclust"
    benomylsetup$algo$method   <- "complete"
    benomylsetup$algo$distance <- "euclidean"

    ## Setup a new goCluster object
    test <- new("goCluster")
    setup(test) <- benomylsetup

    ## Retrieve annotation
    test@data <- execute(test@data, test)

    ## Cluster the dataset
    test@algo <- execute(test@algo, test)

[Package goCluster version 1.0.3 Index]